class_docstr: SigmoidBeta Distribution.
  
  The SigmoidBeta distribution is defined over the real line using parameters
  `concentration1` (aka 'alpha') and `concentration0` (aka 'beta').
  
  This distribution is the transformation of the Beta distribution such that
  Sigmoid(X) ~ Beta(...) => X ~ SigmoidBeta(...).
  
  #### Mathematical Details
  
  The probability density function (pdf) can be derived from the change of
  variables rule. We begin with `g(X) = Sigmoid(X)`, and note that
  
  ```none
  p_x(x) = p_y(g(y)) | g'(y) |.
  ```
  With `g'(y) = Sigmoid(x) ( 1 - Sigmoid(x))`, we arrive at
  
  ```none
  pdf(x; alpha, beta) = Sigmoid(x)^alpha (1 - Sigmoid(x))^beta / B(alpha, beta)
  B(alpha, beta) = Gamma(alpha) Gamma(beta) / Gamma(alpha + beta)
  ```
  where:
  
  * `concentration1 = alpha`
  * `concentration0 = beta`
  * `B(alpha, beta` is the [beta function](
    https://en.wikipedia.org/wiki/Beta_function)
  * `Gamma` is the [gamma function](
    https://en.wikipedia.org/wiki/Gamma_function).
  
  Critically, these parameters lose the relationship to the mean that they have
  under the untransformed Beta distribution. We choose to keep the names to
  draw analogy to the original Beta distribution.
  
  ```none
  concentration1 = alpha
  concentration0 = beta
  ```
  
  Distribution parameters are automatically broadcast in all functions; see
  examples for details.
  
  
  
  The cumlative density function (cdf) can be found by integrating the pdf
  directly from `-infinity` to x:
  
  ```none
   cdf(x; alpha, beta) = I_Sigmoid(x)(alpha + 1, beta + 1) / B(alpha, beta),
  ```
  
  where `I_x(alpha, beta)` is the [incomplete beta function](
  https://en.wikipedia.org/wiki/Beta_function#Incomplete_beta_function).
  
  Samples of this distribution are reparameterized (pathwise differentiable).
  The derivatives are computed using the approach described in
  [(Figurnov et al., 2018)][1].
  
  #### Examples
  
   ```python
   tfd = tfp.distributions
  
   dist = tfd.SigmoidBeta(concentration0=1.,
                          concentration1=2.)
  
   dist.sample([4, 5])  # Shape [4, 5, 3]
  
   # `x` has three batch entries, each with two samples.
   x = [[.1, .4, .5],
        [.2, .3, .5]]
   # Calculate the probability of each pair of samples under the corresponding
   # distribution in `dist`.
   dist.prob(x)         # Shape [2, 3]
   ```
  
  #### References
  
  [1]: Michael Figurnov, Shakir Mohamed, Andriy Mnih.
       Implicit Reparameterization Gradients. _arXiv preprint arXiv:1805.08498_,
       2018. https://arxiv.org/abs/1805.08498
code: def _mode(self):
      return tf.math.log(self.concentration1 / self.concentration0)
  
imports: import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import identity
  from tensorflow_probability.python.bijectors import softplus
  from tensorflow_probability.python.distributions import distribution
  from tensorflow_probability.python.distributions import gamma
  from tensorflow_probability.python.internal import assert_util
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import parameter_properties
  from tensorflow_probability.python.internal import reparameterization
  from tensorflow_probability.python.internal import samplers
  from tensorflow_probability.python.internal import tensor_util
  from tensorflow_probability.python.math import special
label_desc: Use image node_id 15 for calling the SigmoidBeta obj's underlying member method code with example usage: obj._mode() and returns: tf